Joint feature selection and subspace learning

IJCAI International Joint Conference on Artificial Intelligence

Volumn , Issue , 2011, Pages 1294-1299

  Gu, Quanquan ^a   Li, Zhenhui ^a   Han, Jiawei ^a  

^a University of Illinois at Urbana Champaign   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DIMENSIONALITY REDUCTION; OPTIMIZATION ALGORITHMS; PROJECTION MATRIX; RELEVANT FEATURES; STATE-OF-THE-ART METHODS; SUBSPACE LEARNING;

ALGORITHMS; FACE RECOGNITION; LINEAR TRANSFORMATIONS;

ARTIFICIAL INTELLIGENCE;

EID: 84870535269     PISSN: 10450823     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.5591/978-1-57735-516-8/IJCAI11-219     Document Type: Conference Paper

References (18)

1. Andreas Argyriou, Theodoros Evgeniou, and Massimiliano Pontil. Convex multi-task feature learning. Machine Learning, 73(3):243-272, 2008.
2. Peter N. Belhumeur, João P. Hespanha, and David J. Kriegman. Eigenfaces vs. fisherfaces: Recognition using class specific linear projection. IEEE Trans. Pattern Anal. Mach. Intell., 19(7):711-720, 1997.
3. Stephen Boyd and Lieven Vandenberghe. Convex optimization. Cambridge University Press, Cambridge, 2004.
4. Deng Cai, Xiaofei He, and Jiawei Han. Spectral regression: A unified approach for sparse subspace learning. In ICDM, pages 73-82, 2007.
5. Fan R. K. Chung. Spectral Graph Theory. American Mathematical Society, February 1997.
6. Gene H. Golub and Charles F. Van Loan. Matrix computations (3rd ed.). Johns Hopkins University Press, Baltimore, MD, USA, 1996.
7. Isabelle Guyon and André Elisseeff. An introduction to variable and feature selection. Journal of Machine Learning Research, 3:1157-1182, 2003.
8. Xiaofei He and Partha Niyogi. Locality preserving projections. In NIPS, 2003.
9. Xiaofei He, Deng Cai, Shuicheng Yan, and HongJiang Zhang. Neighborhood preserving embedding. In ICCV, pages 1208-1213, 2005.
10. Mahdokht Masaeli, Glenn Fung, and Jennifer G. Dy. From transformation-based dimensionality reduction to feature selection. In ICML, pages 751-758, 2010.
11. Baback Moghaddam, Yair Weiss, and Shai Avidan. Generalized spectral bounds for sparse lda. In ICML, pages 641-648, 2006.
12. 2,1 norms minimization. In Advances in Neural Information Processing Systems 23, pages 1813-1821, 2010.
13. Guillaume Obozinski, Ben Taskar, and Michael I. Jordan. Joint covariate selection and joint subspace selection for multiple classification problems. Statistics and Computing, 20:231-252, April 2010.
14. Terence Sim, Simon Baker, and Maan Bsat. The cmu pose, illumination, and expression database. IEEE Trans. Pattern Anal. Mach. Intell., 25(12):1615-1618, 2003.
15. Liang Sun, Jun Liu, Jianhui Chen, and Jieping Ye. Efficient recovery of jointly sparse vectors. In Advances in Neural Information Processing Systems 22, pages 1812-1820, 2009.
16. Shuicheng Yan, Dong Xu, Benyu Zhang, HongJiang Zhang, Qiang Yang, and Stephen Lin. Graph embedding and extensions: A general framework for dimensionality reduction. IEEE Trans. Pattern Anal. Mach. Intell., 29(1):40-51, 2007.
17. Ming Yuan, Ming Yuan, Yi Lin, and Yi Lin. Model selection and estimation in regression with grouped variables. Journal of the Royal Statistical Society, Series B, 68:49-67, 2006.
18. Hui Zou, Trevor Hastie, and Robert Tibshirani. Sparse principal component analysis. Journal of Computational and Graphical Statistics, 15:2006, 2004.